Robust Analysis of 4e− vs 6e− reduction ofNitrogen on metal surfaces and single atom alloys
Bibliographic record
Abstract
The electrochemical synthesis of hydrazine is an exciting avenue in the sustainable production of commonly used chemicals. Taking inspiration from the mechanistic selectivity of reactions such as 2e- vs 4e- ORR, we explore how to fine tune catalysts for hydrazine synthesis through the 4e- electrochemical nitrogen reduction reaction (NRR) over the popular 6e- (NRR) used for ammonia synthesis. Optimal 4e- NRR performance requires sufficient activity as well as selectivity over 6e- (NRR), other mechanistic NRR reaction branching points and the hydrogen evolution reaction. In this study, we perform first principles calculations in conjunction with uncertainty quantification on various monometallic and single atom alloy surfaces to study activity and selectivity of 4e- NRR. Through free energy diagrams, estimation of scaling relations and a theoretical activity volcano, we observe that catalysts exhibiting low activity due to weak binding for NH3, favor hydrazine synthesis. We also find that single atom alloys follow the same scaling relations as monometallic surfaces. Through uncertainty quantification, we form distributions of limiting potentials and establish a correlation between the activity of a catalyst with the skewness of its limiting potential distribution. We further quantify first principles calculations uncertainty for branching points within various 4e- NRR branching points. Reaction branching point analysis and the tradeoff between activity and selectivity of the catalysts points to the significant challenges of pushing NRR towards hydrazine synthesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".